Assessing the Nonlinear Relationship between Land Cover Change and PM 10 Concentration Change in China

Xiankang Xu, Jian Hao, Yuxin Liang and Jingwei Shen ()
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Xiankang Xu: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geo-Graphical Sciences, Southwest University, Chongqing 400715, China
Jian Hao: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geo-Graphical Sciences, Southwest University, Chongqing 400715, China
Yuxin Liang: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geo-Graphical Sciences, Southwest University, Chongqing 400715, China
Jingwei Shen: Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geo-Graphical Sciences, Southwest University, Chongqing 400715, China

Land, 2024, vol. 13, issue 6, 1-22

Abstract: Inhalable particulate matter (PM 10 ) is a major air pollutant that has significant impacts on environmental climate and human health. Land-cover change is also a key factor influencing changes in atmospheric pollution. Changes in land-cover types can lead to changes in the sources and sinks of air pollutants, thus affecting the spatial distribution of PM 10 , which poses a threat to human health. Therefore, exploring the relationship between PM 10 concentration change and land-cover change is of great significance. In this study, we constructed an extreme randomized trees model (ET) based on ground PM 10 monitoring data, satellite-based aerosol optical depth (AOD) data, and auxiliary data including meteorological, vegetation, and population data to retrieve ground-level PM 10 concentrations across China. The coefficient of determination (R 2 ), the mean absolute error (MAE), and the root mean square error (RMSE) of the model were 0.878, 5.742 μg/m 3 , and 8.826 μg/m 3 , respectively. Based on this, we analyzed the spatio-temporal distribution of PM 10 concentrations in China from 2015 to 2021. High PM 10 values were mainly observed in the desert areas of northwestern China and the Beijing–Tianjin–Hebei urban agglomeration. The majority of China showed a significant decrease in PM 10 concentrations. Additionally, we also analyzed the nonlinear response mechanism of the PM 10 concentration change to land-cover change. The PM 10 concentration is sensitive to forest and barren land change. Therefore, strengthening the protection of forests and desertification control can significantly reduce air pollution. Attention should also be paid to emission management in agricultural activities and urbanization processes.

Keywords: PM 10; machine learning; land cover; GAM (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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